Geobacillus thermoglucosidasius KP 1022 is a thermophilic prokaryote that was isolated from soil.
thermophilic genome sequence 16S sequence| @ref 20215 |
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| Domain Bacillati |
| Phylum Bacillota |
| Class Bacilli |
| Order Caryophanales |
| Family Bacillaceae |
| Genus Geobacillus |
| Species Geobacillus thermoglucosidasius |
| Full scientific name Geobacillus thermoglucosidasius (Suzuki 1984) Nazina et al. 2001 |
| Synonyms (4) |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 1117 | BACILLUS THERMOGLUCOSIDASIUS MEDIUM (DSMZ Medium 305) | Medium recipe at MediaDive | Name: BACILLUS THERMOGLUCOSIDASIUS MEDIUM (DSMZ Medium 305) Composition: Agar 30.0 g/l Starch 10.0 g/l Peptone 5.0 g/l KH2PO4 3.0 g/l Yeast extract 3.0 g/l Meat extract 3.0 g/l Distilled water |
| @ref | Growth | Type | Temperature (°C) | Range | |
|---|---|---|---|---|---|
| 1117 | positive | growth | 55 | thermophilic |
| @ref | Sample type | Geographic location | Country | Country ISO 3 Code | Continent | |
|---|---|---|---|---|---|---|
| 1117 | soil | Kyoto, Shimogamo | Japan | JPN | Asia |
Global distribution of 16S sequence FN428670 (>99% sequence identity) for Bacillaceae from Microbeatlas ![]()
| @ref | Description | Assembly level | INSDC accession | BV-BRC accession | IMG accession | NCBI tax ID | Score | |
|---|---|---|---|---|---|---|---|---|
| 66792 | ASM1421862v1 assembly for Parageobacillus thermoglucosidasius DSM 2543 | contig | 1426 | 76.18 |
| @ref | Description | Accession | Length | Database | NCBI tax ID | |
|---|---|---|---|---|---|---|
| 1117 | Geobacillus thermoglucosidasius partial 16S rRNA gene, strain R-32629 | FN428670 | 1511 | 1426 |
| 1117 | GC-content (mol%)45.0 |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | yes | 85.50 | no |
| 125439 | motility | BacteriaNetⓘ | yes | 77.80 | no |
| 125439 | gram_stain | BacteriaNetⓘ | variable | 84.90 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | facultative anaerobe | 60.80 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | yes | 74.59 | no |
| 125438 | anaerobic | anaerobicⓘ | no | 84.08 | no |
| 125438 | spore-forming | spore-formingⓘ | yes | 91.58 | no |
| 125438 | aerobic | aerobicⓘ | yes | 54.85 | no |
| 125438 | thermophilic | thermophileⓘ | yes | 65.43 | no |
| 125438 | flagellated | motile2+ⓘ | yes | 81.26 | no |
| Topic | Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|---|
| Genetics | Elucidating the biotechnological potential of the genera Parageobacillus and Saccharococcus through comparative genomic and pan-genome analysis. | Mol M, de Maayer P. | BMC Genomics | 10.1186/s12864-024-10635-1 | 2024 | |
| Not All That Glitters Is Gold: The Paradox of CO-dependent Hydrogenogenesis in Parageobacillus thermoglucosidasius. | Aliyu H, de Maayer P, Neumann A. | Front Microbiol | 10.3389/fmicb.2021.784652 | 2021 | ||
| Genomic identification of germinant receptors and phenotypic response to germinants in Parageobacillus and Geobacillus spp. strains. | Salvador M, Yruela I, Condon S, Gayan E. | Curr Res Microb Sci | 10.1016/j.crmicr.2025.100461 | 2025 | ||
| Metabolism | Comparative genomic analysis of Parageobacillus thermoglucosidasius strains with distinct hydrogenogenic capacities. | Mohr T, Aliyu H, Kuchlin R, Zwick M, Cowan D, Neumann A, de Maayer P | BMC Genomics | 10.1186/s12864-018-5302-9 | 2018 |
| #1117 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 2543 |
| #20215 | Parte, A.C., Sardà Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Göker, M.: List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. IJSEM ( DOI 10.1099/ijsem.0.004332 ) |
| #66792 | Julia Koblitz, Joaquim Sardà, Lorenz Christian Reimer, Boyke Bunk, Jörg Overmann: Automatically annotated for the DiASPora project (Digital Approaches for the Synthesis of Poorly Accessible Biodiversity Information) . |
| #69479 | João F Matias Rodrigues, Janko Tackmann,Gregor Rot, Thomas SB Schmidt, Lukas Malfertheiner, Mihai Danaila,Marija Dmitrijeva, Daniela Gaio, Nicolas Näpflin and Christian von Mering. University of Zurich.: MicrobeAtlas 1.0 beta . |
| #125438 | Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg Overmann: Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets. 2024 ( DOI 10.1101/2024.08.12.607695 ) |
| #125439 | Philipp Münch, René Mreches, Martin Binder, Hüseyin Anil Gündüz, Xiao-Yin To, Alice McHardy: deepG: Deep Learning for Genome Sequence Data. R package version 0.3.1 . |
| #126262 | A. Lissin, I. Schober, J. F. Witte, H. Lüken, A. Podstawka, J. Koblitz, B. Bunk, P. Dawyndt, P. Vandamme, P. de Vos, J. Overmann, L. C. Reimer: StrainInfo—the central database for linked microbial strain identifiers. ( DOI 10.1093/database/baaf059 ) |
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https://doi.org/10.13145/bacdive1431.20251217.10
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BacDive in 2025: the core database for prokaryotic strain data